A tutorial on methods for the modeling and analysis of social network data

Abstract

This article provides a tutorial review of some fundamental ideas and important methods for the modeling of empirical social network data. It describes basic concepts from graph theory and central elements from social network theory. It presents models for the network degree distribution and for network roles and positions, as well as algebraic approaches, before reviewing recent work on statistical methods to analyze social networks, including boot-strap procedures for testing the prevalence of network structures, basic edge- and dyad-independent statistical models, and more recent statistical network models that assume dependence, exponential random graph models and dynamic stochastic actor oriented models. Network social influence models are reviewed. The article concludes with a summary of new developments relating to models for time-ordered transactions.